Identifying and comparing vacant housing determinants across South Korean cities

Park, Yunmi; Newman, Galen; Lee, Jung Eun; Lee, Suk Jin

doi:10.1016/j.apgeog.2021.102566

Cited by 17 publications

(11 citation statements)

References 39 publications

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“…We then predicted the areas likely to have vacant properties in the future using a multilevel logistic regression model and explored measures to tackle housing vacancy issues. Our analysis provides implications for a more objective and systematic response, as we examined the status of vacant properties with two different levels of variables: (1) individual building level such as parcel size, shape, construction year, and access to infrastructure, and (2) the Dong, which is the smallest administrative unit in Korea, sharing relatively homogeneous socioeconomic conditions such as population density, employed population, and fertility rate [32].…”

Section: Housing Vacancy Databasementioning

confidence: 99%

“…The established building database includes various charac teristics, including building identification number, parcel number, building structure land area, gross floor area, violated building, building area, height, and construction yea Based on the parcel numbering unit code for vacant properties derived from the above process, we established a building database along with building data from the geographic information system. The established building database includes various characteristics, including building identification number, parcel number, building structure, land area, gross floor area, violated building, building area, height, and construction year [1]. These data can be used to analyze contributors to vacant properties.…”

Section: Housing Vacancy Databasementioning

confidence: 99%

“…Recent rapid urban growth has forced Korean cities to focus on physical and spatial expansion [1]. As such, planning policies related to growth management or smart growth are being developed with sustainability and development in mind [2].…”

Section: Introductionmentioning

confidence: 99%

“…The primary objectives of this research are twofold: (1) to accurately analyze contributors to vacant properties and spatial distribution through a multilevel model design based on data for individual parcels, and (2) to provide evidence to establish policies to boost the local area by predicting areas at risk of vacancy. Our study differs from the prevailing works on vacant properties in three ways.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Detached Housing Vacancy: A Multilevel Analysis

2022

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Urban shrinkage is a critical issue in local small- and medium-sized cities in Korea. While there have been several studies to analyze the causes and consequences of vacancy increases, most have only focused on socioeconomic associations at larger scale and failed to consider individual housing level characteristics, primarily due to a lack of appropriate data. Based on data including 52,400 individual parcels, this study analyzes the primary contributors to vacant properties and their spatial distribution through a multilevel model design based on data for each parcel. Then, we identify areas at high risk of vacancy in the future to provide evidence to establish policies for improving the local environment. Results indicate that construction year, building structure, and road access conditions have a significant effect on vacant properties at the individual parcel level, and the presence of schools and hypermarket within 500 m are found to decrease vacant properties. Further, prediction outcomes show that the aged city center and areas with strict regulations on land use are expected to have a higher vacancy rate. These findings are used to provide a set of data-based revitalization strategies through the development of a vacancy prediction model.

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Section: Housing Vacancy Databasementioning

confidence: 99%

Section: Housing Vacancy Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Detached Housing Vacancy: A Multilevel Analysis

2022

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“…Residential quarters with poor location conditions are also prone to abandonment [22]-the possibility of abandonment increases if a residential quarter is distant from nearby facilities (e.g., schools, convenience stores, hospitals) and has poor transportation [16]. Residential quarters with a high elderly population have a higher probability of abandonment [22][23][24]. Residential quarters with poor neighborhood environmental characteristics (i.e., poor resources and services, disharmonious emotional relationships between neighbors, and even the presence of negative factors such as high crime rates [25]) are prone to abandonment [26], leading to a lack of attachment to the community and prompting residents to move out.…”

Section: Et Al Used a Large Sample Of Hangzhou To Analyze Spatialmentioning

confidence: 99%

Spatial Characteristics of the Abandonment Degree of Residential Quarters Based on Data of the Housing Sales Ratio—A Case Study of Kunming, China

Wang

Yue

et al. 2022

Buildings

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The abandonment degree of an urban residential building can reflect the popularity of residential areas. This study uses this idea as a basis for proposing the concept of using residential quarters’ abandonment rate to measure the abandonment degree of an area. The spatial pattern of the abandonment rate and its clustering characteristics were obtained by taking 2517 residential quarters in Kunming’s Main Urban District as research object, and using their listing for sale ratio data. Thereafter, curve estimation was used to explore the influencing factors of abandonment rate. The results are as follows. (1) The abandonment rate of the four circles in Kunming’s Main Urban District increases from inside to outside, showing the pattern of the core area–second ring area–third ring area–new urban district, with evident “core–edge” characteristics. (2) The relationship between distance from the city center, housing ages, and abandonment rate can be well fitted using a quadratic function and shows an inverted “U”-shaped “rising–declining” trend. The relationship between housing prices and abandonment rate is fitted by the inverse function, showing an evident “up” trend. This study is a reference for managers of relevant departments and urban planners in formulating reasonable urban housing development policies.

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